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Record W2808852712 · doi:10.1002/hec.3786

The impact of the 2008/2009 financial crisis on specialist physician activity in Canada

2018· article· en· W2808852712 on OpenAlexafffundabout
M. Ruth Lavergne, Lindsay Hedden, Michael R. Law, Kim McGrail, Megan Ahuja

Bibliographic record

VenueHealth Economics · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of British ColumbiaVancouver Coastal HealthSimon Fraser University
FundersMichael Smith Health Research BC
KeywordsPaymentFinancial crisisService (business)PopulationNatural experimentFinancePhysician supplyBusinessDemographic economicsPopulation ageingMedicineDemographyEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

Fee-for-service physicians are responsible for planning for their retirements, and there is no mandated retirement age. Changes in financial markets may influence how long they remain in practice and how much they choose to work. The 2008 crisis provides a natural experiment to analyze elasticity in physician service supply in response to dramatic financial market changes. We examined quarterly fee-for-service data for specialist physicians over the period from 1999/2000 to 2013/2014 in Canada. We used segmented regression to estimate changes in the number of physicians receiving payments, per-physician service counts, and per-physician payments following the 2008 financial crisis and explored whether patterns differed by physician age. The number of specialist physicians increased more rapidly in the period since 2008 than in earlier years, but increases were largest within the youngest age group, and we observed no evidence of delayed retirement among older physicians. Where changes in service volume and payments were observed, they occurred across all ages and not immediately following the 2008 financial crisis. We conclude that any response to the financial crisis was small compared with demographic shifts in the physician population and changes in payments per service over the same time period.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.410
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2018
Admission routes3
Has abstractyes

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